Gastos Casa
09 December, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 1/10/2024 | Electricidad | 88000 | Andrés | NA |
| 1/10/2024 | Comida | 22895 | Andrés | piwen |
| 2/10/2024 | Viaje Brasil | 277776 | Tami | Cuota que faltaba viaje Brasil |
| 5/10/2024 | Comida | 50577 | Tami | Supermercado |
| 6/10/2024 | Agua | 11723 | Andrés | NA |
| 9/10/2024 | Diosi | 14990 | Andrés | comida diosi nyd 1.5k |
| 12/10/2024 | Comida | 70144 | Tami | Supermercado |
| 19/10/2024 | Comida | 62351 | Tami | Supermercado |
| 24/10/2024 | Electrodomésticos/mantención casa | 13500 | Andrés | lona quitasol |
| 24/10/2024 | VTR | 21990 | Andrés | NA |
| 25/10/2024 | Comida | 8350 | Tami | Supermercado |
| 27/10/2024 | Comida | 78084 | Tami | Supermercado |
| 28/10/2024 | Diosi | 15000 | Andrés | NA |
| 29/10/2024 | Electricidad | 50227 | Andrés | NA |
| 31/10/2024 | Electrodomésticos/mantención casa | 13264 | Andrés | lona sombreadora |
| 31/10/2024 | Agua | 12000 | Andrés | NA |
| 4/11/2024 | Comida | 64915 | Tami | Supermercado |
| 7/11/2024 | Comida | 34229 | Andrés | piwen |
| 8/11/2024 | Assistcard viaje | 44836 | Tami | Assistcard viaje |
| 9/11/2024 | Comida | 35916 | Tami | Supermercado |
| 15/11/2024 | Comida | 12576 | Tami | Supermercado |
| 17/11/2024 | Comida | 91203 | Tami | Supermercado |
| 18/11/2024 | Diosi | 49500 | Tami | Veterinaria |
| 23/11/2024 | Comida | 50929 | Tami | Supermercado |
| 26/11/2024 | Enceres | 12990 | Tami | Confort 48 rollos |
| 27/11/2024 | Transporte | 21780 | Tami | Transvip aeropuerto |
| 27/11/2024 | Electricidad | 45000 | Andrés | Eléctrico |
| 30/11/2024 | Comida | 62899 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0001e+09 2 4.9411 0.0074 **
## lag_depvar 2.5880e+11 1 2557.3413 <2e-16 ***
## Residuals 7.9441e+10 785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -2027.736 16485.41 0.1592822
## 2-0 31805.987 23459.768 40152.21 0.0000000
## 2-1 24577.149 19727.226 29427.07 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 633 77359.43 2 88593.71
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## 635 81753.00 2 79878.71
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## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
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## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
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## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
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## 670 45382.00 2 41907.86
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## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
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## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
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## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
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## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 633 54040.25 22739.689
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2023.897867 4042.519522 -540.168680 2436.253821 -2973.778075
## 7 8 9 10 11
## 517.238971 -5658.064194 -1185.195778 -3963.250311 -412.355488
## 12 13 14 15 16
## -4934.891849 -1601.219142 -891.597940 384.977556 -3237.074103
## 17 18 19 20 21
## -370.357169 -2123.590712 6611.308571 -1529.441691 -1207.621964
## 22 23 24 25 26
## 1476.806536 -1187.370542 234.569247 1694.266416 -7104.654910
## 27 28 29 30 31
## 951.301688 8194.490299 412.393897 -19.768831 -2405.947996
## 32 33 34 35 36
## 1573.329167 4568.250253 1118.788414 2383.027014 -1877.800047
## 37 38 39 40 41
## 4600.730184 4299.512471 -2282.980987 -2985.754218 -1111.255547
## 42 43 44 45 46
## -10741.447164 7298.964211 2559.048119 1366.074777 8103.231219
## 47 48 49 50 51
## 677.163814 6520.274932 6701.109613 -5899.976315 -4805.572121
## 52 53 54 55 56
## -5064.278926 -7928.003766 6137.415981 -4075.516320 -4889.741632
## 57 58 59 60 61
## 3864.169568 891.613072 -29.665820 144.344366 -4994.756757
## 62 63 64 65 66
## 18132.697914 3629.489127 -3659.147033 5917.278760 7331.702631
## 67 68 69 70 71
## 14621.589102 1665.056292 -13238.703783 -1316.776620 4635.502308
## 72 73 74 75 76
## -4911.400555 -4409.716802 -10497.859323 2476.318246 -5393.552030
## 77 78 79 80 81
## 1074.328409 -6857.884082 560.731134 -2342.893623 -2681.356164
## 82 83 84 85 86
## -3918.319761 -521.886108 2328.875721 3773.010025 482.366814
## 87 88 89 90 91
## -480.289754 200.717104 4305.279432 -1164.176336 1150.877315
## 92 93 94 95 96
## -2065.562361 -1043.344228 179.364464 276.144196 -7483.136938
## 97 98 99 100 101
## 2399.651802 -8597.805421 -2928.149125 -4025.570587 -1720.466112
## 102 103 104 105 106
## -1245.205172 3196.480965 -2331.432872 2605.601003 -1150.737766
## 107 108 109 110 111
## 978.293806 2593.022237 -3151.687266 -4717.660464 -840.977034
## 112 113 114 115 116
## 1912.509362 11699.611522 -1248.492348 2664.564643 4256.855271
## 117 118 119 120 121
## 3493.429167 -1111.301233 -4725.132850 -3727.198820 2320.706559
## 122 123 124 125 126
## -1733.971109 1341.002674 8857.541636 838.094228 121.835267
## 127 128 129 130 131
## -2528.851179 2650.911963 7046.419268 1000.438243 -8510.804841
## 132 133 134 135 136
## 1747.384656 4132.285775 -3170.694867 -1422.574027 -855.070753
## 137 138 139 140 141
## -3880.107895 1186.713884 -493.361738 -2911.246469 1723.046331
## 142 143 144 145 146
## -1878.433101 -7825.168140 2050.591676 -3471.932723 2112.345084
## 147 148 149 150 151
## -250.693992 1029.150817 -354.983596 1356.262865 1188.842404
## 152 153 154 155 156
## 3357.324828 -4864.395554 -1172.104566 -3232.628523 5962.592557
## 157 158 159 160 161
## 9746.033591 -3691.661128 -5036.048524 3349.459546 -65.232389
## 162 163 164 165 166
## 2434.565377 -6176.281700 -7008.027592 3901.611467 17130.548667
## 167 168 169 170 171
## 3338.438963 -692.131751 -2737.781818 -1391.582637 3305.066787
## 172 173 174 175 176
## -518.333446 -8364.530180 2585.994674 4042.931242 336.638227
## 177 178 179 180 181
## 8460.969600 -9549.857225 -3758.502757 -11026.591974 -11507.885462
## 182 183 184 185 186
## 980.289686 9032.967985 -1706.970031 5652.041205 6267.605499
## 187 188 189 190 191
## 12858.380554 8108.147115 -4399.758384 2135.562590 10035.437171
## 192 193 194 195 196
## -1993.780737 -2789.486636 -10619.074034 -6682.136794 926.276357
## 197 198 199 200 201
## -5542.814413 -10096.096344 5100.323201 -3362.304242 -2001.719895
## 202 203 204 205 206
## -1091.622948 6206.669207 9579.125377 253.817987 2599.144780
## 207 208 209 210 211
## 2767.422300 5449.234427 12489.472609 -6052.926147 -11645.262372
## 212 213 214 215 216
## -5989.686395 -10897.882090 -5364.552574 1246.147173 -13295.890102
## 217 218 219 220 221
## 16127.492611 7511.192363 1212.882565 26369.898249 12153.243093
## 222 223 224 225 226
## 6941.195451 13625.245905 -4335.132406 -2143.886802 3387.267639
## 227 228 229 230 231
## -28.954381 2365.767945 8627.983966 5446.249903 -2289.732361
## 232 233 234 235 236
## -2197.493544 9067.923196 -11877.969707 -7623.638664 -8863.612611
## 237 238 239 240 241
## -10405.753008 2792.638423 1059.326002 -8593.475321 -9270.417815
## 242 243 244 245 246
## 8829.166867 -8052.971974 2213.232885 -10583.524938 -4318.329263
## 247 248 249 250 251
## 1161.850845 734.176682 -12591.261647 3389.147020 1794.600125
## 252 253 254 255 256
## 3934.591118 1844.482838 -1458.377360 10841.447495 20555.956587
## 257 258 259 260 261
## 2823.300228 -4648.929164 3745.768437 -2064.217239 3375.547872
## 262 263 264 265 266
## -5218.269040 -11245.055748 -5052.699323 -834.951650 -5501.358394
## 267 268 269 270 271
## 8475.482516 -4606.883975 3871.949076 -2436.411474 4105.570993
## 272 273 274 275 276
## 371.545777 6963.412831 -1770.326540 11671.505531 -4969.015077
## 277 278 279 280 281
## 1354.869132 -745.305298 7481.866055 -5445.663044 -3101.064371
## 282 283 284 285 286
## -11619.972849 -2993.379411 18338.197899 7412.986595 2344.095457
## 287 288 289 290 291
## -1022.059889 519.141224 6013.015447 6482.757702 -19186.507116
## 292 293 294 295 296
## -11487.657517 -8431.966595 9380.280596 2755.866739 -1504.653175
## 297 298 299 300 301
## 27080.746210 9657.377101 4468.171515 9079.604976 2398.729547
## 302 303 304 305 306
## -1486.390456 7458.690367 -24746.857005 -3891.504404 -514.103217
## 307 308 309 310 311
## -7302.012638 -4278.147574 2641.067769 -9492.906373 -3497.810055
## 312 313 314 315 316
## -8443.732633 1334.433676 -3392.419246 1813.355800 -4330.627086
## 317 318 319 320 321
## 27206.122638 -1086.702381 2933.650833 10463.561256 5187.366165
## 322 323 324 325 326
## 31966.486454 4592.845586 -21450.600208 1376.115422 699.481517
## 327 328 329 330 331
## -6868.737725 -2103.734990 -33623.168618 690.859338 -2498.609514
## 332 333 334 335 336
## -283.024452 -3360.708395 3901.074761 -642.554828 -7159.919981
## 337 338 339 340 341
## -3300.261599 -2368.602466 -7854.014088 3700.735338 -1547.062646
## 342 343 344 345 346
## -1915.523208 -1171.755760 -4.336529 292.993508 -1815.791545
## 347 348 349 350 351
## -9643.681214 -13376.048511 2187.395355 -4468.493136 -3797.216832
## 352 353 354 355 356
## -6115.275909 1627.711973 1240.086964 2590.332886 -3952.583419
## 357 358 359 360 361
## -696.366777 489.604537 6813.906448 38.724299 -281.767263
## 362 363 364 365 366
## 2336.002439 -3011.427851 -1127.226524 -8990.432728 -4834.453754
## 367 368 369 370 371
## -6403.777624 -5118.310299 -7406.777837 4883.746026 203.748671
## 372 373 374 375 376
## 6940.627104 -7856.831027 -2452.969707 -3573.800192 -2643.934269
## 377 378 379 380 381
## -12630.348373 1778.356434 -10780.272905 5586.835615 9184.951358
## 382 383 384 385 386
## 2919.112222 -2627.854453 1381.171084 6507.289811 11137.070484
## 387 388 389 390 391
## -6133.001562 -5667.438469 -438.529987 8281.002605 1490.298312
## 392 393 394 395 396
## 10889.763561 -10262.407925 2440.812680 368.171820 217.818798
## 397 398 399 400 401
## -997.465890 -899.970965 -14818.096992 8270.335795 -1471.590639
## 402 403 404 405 406
## -1654.043888 6709.172038 -8238.211783 -1558.832494 -2784.593214
## 407 408 409 410 411
## -6057.499462 -3067.258761 -4112.758648 -8934.772829 5991.925702
## 412 413 414 415 416
## 1458.089108 -7571.356747 -7859.052913 14084.311644 3593.201881
## 417 418 419 420 421
## 4241.151271 -8314.786838 -4982.356284 -2816.205911 2615.394538
## 422 423 424 425 426
## -14233.691342 -2946.498731 -9247.751716 2900.688475 6836.260260
## 427 428 429 430 431
## 6386.923659 -4217.789543 -4334.392675 -4918.948191 -1967.793142
## 432 433 434 435 436
## -5887.966725 -6781.523899 -6080.888632 -1506.737998 -967.912034
## 437 438 439 440 441
## -5104.189543 2464.726416 4695.688628 -5236.463249 -2320.802124
## 442 443 444 445 446
## 1415.331370 -4015.342470 2668.719483 -6767.719920 -12274.043920
## 447 448 449 450 451
## -4624.490380 9540.975871 -2196.599688 4591.741370 -6063.169974
## 452 453 454 455 456
## -1292.833446 212.155497 2845.922240 -12469.413143 3222.107883
## 457 458 459 460 461
## -6873.488106 6374.754205 2824.673465 2299.640770 -4068.639101
## 462 463 464 465 466
## 1886.176464 -227.216004 1570.964378 -753.698393 3119.413548
## 467 468 469 470 471
## -2887.098936 5570.988304 -7203.373630 -3188.725802 -2414.828749
## 472 473 474 475 476
## -4863.395847 2817.051523 7599.583709 -6254.365363 1276.460305
## 477 478 479 480 481
## -6394.910931 -3032.057245 1834.390160 -13121.152774 -9891.026040
## 482 483 484 485 486
## -1303.420329 -88.263779 -1083.058446 -1469.434716 -9716.669297
## 487 488 489 490 491
## 10996.154618 6072.669362 7222.285303 -5671.998446 5156.905407
## 492 493 494 495 496
## 9055.983532 5774.741983 -13775.799926 -10799.005268 -3624.469476
## 497 498 499 500 501
## -1276.637213 -694.584111 -7798.438129 468.957535 4135.759620
## 502 503 504 505 506
## 5331.002190 453.742070 -130.366483 -7451.086101 389.933189
## 507 508 509 510 511
## -5234.377157 1666.148447 -1475.933640 -8335.293365 -747.288126
## 512 513 514 515 516
## -2824.263019 -732.525403 1182.564138 -9657.781812 -7891.892200
## 517 518 519 520 521
## 24184.149859 9602.875444 5612.475138 -5625.217465 2538.516683
## 522 523 524 525 526
## 16749.849058 11131.814523 -24531.482573 -5318.955507 -3966.025926
## 527 528 529 530 531
## 4357.315682 -592.998839 -11336.348783 4208.525110 13703.218282
## 532 533 534 535 536
## -5247.606208 4128.834568 5291.536090 -2076.010936 -4813.160162
## 537 538 539 540 541
## -7321.186413 -2312.947250 8119.344107 -114.689079 -8381.900520
## 542 543 544 545 546
## 1614.126427 -811.066097 156.796986 -11242.869494 -11224.974836
## 547 548 549 550 551
## 1921.210224 6864.992627 -1495.455532 660.720818 -7904.988984
## 552 553 554 555 556
## 8411.472080 708.395240 -12149.108715 9008.368358 8455.133034
## 557 558 559 560 561
## -145.706303 4608.815614 -3839.517180 13862.337531 21190.033552
## 562 563 564 565 566
## -6865.576921 -10034.817725 6476.664596 -95.888883 3140.774282
## 567 568 569 570 571
## -7697.953208 -17585.001870 6471.634128 6213.185063 1653.807754
## 572 573 574 575 576
## 2845.382773 1507.667401 -2429.851224 14467.305565 -9961.454133
## 577 578 579 580 581
## -6505.713543 8482.061575 2591.614716 -6820.186284 7269.130290
## 582 583 584 585 586
## -4070.433424 -3023.684785 15470.449509 -14799.428667 8199.745780
## 587 588 589 590 591
## -193.736075 -6471.431908 -980.335567 31.473496 -10873.093508
## 592 593 594 595 596
## 1625.694566 -7326.465456 2914.986507 8695.135362 -7714.474417
## 597 598 599 600 601
## 5670.710917 2525.570921 6634.188604 -3440.753585 5917.012134
## 602 603 604 605 606
## -8555.272234 2039.928761 1046.034654 2910.908698 1256.685817
## 607 608 609 610 611
## 156.129274 -6049.425491 7865.081661 -1427.067150 -2807.475217
## 612 613 614 615 616
## -3671.362166 -8428.606586 11794.638267 4718.569035 -9550.952025
## 617 618 619 620 621
## 11433.896402 5808.219053 -5835.077398 26129.133664 -13159.847725
## 622 623 624 625 626
## -7043.060831 2931.063494 -4393.921836 -10804.395123 11130.859857
## 627 628 629 630 631
## -21849.465723 -2538.933637 8554.313671 10972.817932 -1762.730122
## 632 633 634 635 636
## 33082.786130 -6920.105183 5426.483818 5096.999269 -2579.550821
## 637 638 639 640 641
## -5633.196390 -2195.304190 -12670.693327 -2426.874039 -2061.804548
## 642 643 644 645 646
## -2689.287700 -3018.597614 1663.244226 4268.444072 16782.427157
## 647 648 649 650 651
## 18303.040948 566.004540 4481.160506 10297.444562 19808.504389
## 652 653 654 655 656
## 345.448790 -28438.848611 -1585.267965 -2520.021742 1656.909222
## 657 658 659 660 661
## -3403.213072 -10814.173185 1515.620947 4070.740414 -1177.662407
## 662 663 664 665 666
## 12866.383478 1148.810483 1602.705449 -11902.643566 1213.969876
## 667 668 669 670 671
## 1018.450595 -5337.251692 -7561.228957 1941.384626 -3846.370715
## 672 673 674 675 676
## 2549.523046 -3514.875982 -9463.243579 -8406.229549 -3059.804590
## 677 678 679 680 681
## 89.551867 2753.664391 602.786099 -3945.003486 -1919.801814
## 682 683 684 685 686
## -1429.706047 -8355.511305 4555.237645 -2358.058854 -1512.250519
## 687 688 689 690 691
## 472.367427 10731.594326 9690.572351 10435.914135 -9876.391931
## 692 693 694 695 696
## -3728.707974 -3299.912113 5721.981207 -10551.102539 -8043.627041
## 697 698 699 700 701
## -8721.822723 -6364.964362 -4819.615895 3006.037858 -4495.788298
## 702 703 704 705 706
## -1987.015435 4130.990826 30997.138954 9348.621225 23268.259924
## 707 708 709 710 711
## 1483.902996 8140.498734 22741.283296 6368.644078 -18384.710208
## 712 713 714 715 716
## 4681.640675 -5581.281748 -224.483239 360.434927 -17382.942510
## 717 718 719 720 721
## -5356.884488 3248.404639 -3104.671520 -13066.107152 4207.116196
## 722 723 724 725 726
## -5636.639552 667.085876 -4013.332928 -12523.095353 1304.529777
## 727 728 729 730 731
## -1935.795359 -9847.302128 17207.841912 1688.788661 -2812.415171
## 732 733 734 735 736
## 5628.294595 -8725.294703 -807.470478 8053.890678 -15447.023362
## 737 738 739 740 741
## -5987.639014 7336.410809 -4868.271120 81.467607 1746.166195
## 742 743 744 745 746
## -2041.340897 -5252.334575 6333.306489 -6364.058881 22616.590902
## 747 748 749 750 751
## 7716.600771 -2064.258795 -7399.881415 23315.974072 -4416.805500
## 752 753 754 755 756
## 1280.898649 -14535.469747 56015.295296 26771.325015 14931.247635
## 757 758 759 760 761
## -10810.455739 10467.414567 7174.528212 5672.214289 -46522.595071
## 762 763 764 765 766
## -16250.044189 903.901867 -2582.252107 -3521.147895 122782.778273
## 767 768 769 770 771
## 19148.199604 43555.972286 22401.201204 11890.770449 15668.694007
## 772 773 774 775 776
## 25551.608045 -98990.072392 -6846.206460 -35912.595810 1685.986496
## 777 778 779 780 781
## -1283.302381 3341.042137 -7473.803092 -1499.056397 -1999.611330
## 782 783 784 785 786
## 3370.256143 -7213.242684 -2289.974418 3866.656744 2208.347602
## 787 788 789 790
## -2818.321300 -4104.693763 1684.310054 2796.776841
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17245.39 20096.48 24356.31 24073.89 26430.49 23759.48 24476.78 19702.34
## 10 11 12 13 14 15 16 17
## 19438.54 16777.64 17556.18 14281.08 14332.31 14997.88 16696.79 15014.50
## 18 19 20 21 22 23 24 25
## 16050.59 15423.26 22515.44 21598.19 21077.34 22969.94 22295.00 22948.45
## 26 27 28 29 30 31 32 33
## 24796.94 18716.98 20445.51 28293.61 28351.34 28023.81 25649.96 27054.32
## 34 35 36 37 38 39 40 41
## 30902.64 31251.54 32662.66 30169.84 34143.49 37355.98 34408.04 31214.54
## 42 43 44 45 46 47 48 49
## 30060.73 20627.32 28156.38 30596.21 31686.91 38534.41 38028.30 42696.89
## 50 51 52 53 54 55 56 57
## 46938.98 39626.86 34187.85 29203.72 22338.73 28637.37 25213.31 21505.83
## 58 59 60 61 62 63 64 65
## 25920.24 27181.52 27478.94 27891.33 23756.59 40370.65 42217.15 37456.58
## 66 67 68 69 70 71 72 73
## 41669.30 46591.70 57274.52 55285.56 40508.49 38010.93 41032.97 35325.29
## 74 75 76 77 78 79 80 81
## 30771.29 21461.97 24667.84 20587.96 22676.88 17565.41 19583.61 18809.07
## 82 83 84 85 86 87 88 89
## 17835.46 15901.74 17181.27 20794.28 25218.06 26209.29 26234.28 26851.86
## 90 91 92 93 94 95 96 97
## 30982.60 29811.55 30812.28 28874.06 28072.78 28441.43 28848.57 22417.21
## 98 99 100 101 102 103 104 105
## 25436.38 18457.29 17311.86 15349.89 15650.06 16328.38 20807.15 19889.40
## 106 107 108 109 110 111 112 113
## 23405.31 23194.99 24873.41 27754.12 25248.80 21687.41 21963.20 24613.10
## 114 115 116 117 118 119 120 121
## 35492.49 33682.86 35522.86 38525.29 40483.87 38169.13 32983.06 29319.44
## 122 123 124 125 126 127 128 129
## 31405.11 29682.71 30865.89 38476.05 38118.02 37178.28 34037.52 35821.15
## 130 131 132 133 134 135 136 137
## 41226.42 40665.95 31855.62 33122.14 36316.27 32722.00 31107.07 30190.82
## 138 139 140 141 142 143 144 145
## 26743.14 28159.50 27928.82 25611.95 27639.15 26262.03 19855.41 22890.08
## 146 147 148 149 150 151 152 153
## 20713.80 23694.98 24235.71 25828.27 26010.59 27667.01 28969.53 32005.82
## 154 155 156 157 158 159 160 161
## 27469.82 26731.77 24283.69 30185.82 41712.09 40040.05 37401.40 42428.52
## 162 163 164 165 166 167 168 169
## 43839.01 47259.57 42719.31 38020.10 43452.74 59777.13 61992.27 60404.21
## 170 171 172 173 174 175 176 177
## 57225.58 55622.65 58328.90 57351.67 49633.29 52460.64 56208.36 56244.60
## 178 179 180 181 182 183 184 185
## 63383.14 53872.50 50619.02 41415.17 32943.00 36456.03 46573.26 46028.53
## 186 187 188 189 190 191 192 193
## 51989.39 57742.19 68539.85 73829.90 67516.01 67709.71 74789.64 70460.20
## 194 195 196 197 198 199 200 201
## 65976.93 55206.14 49228.15 50654.39 46243.10 38401.25 44834.73 43059.72
## 202 203 204 205 206 207 208 209
## 42697.19 43176.19 49979.45 58880.75 58509.86 60237.01 61895.05 65691.38
## 210 211 212 213 214 215 216 217
## 75170.78 67242.83 55415.83 50017.31 41001.41 37955.00 41072.89 31079.51
## 218 219 220 221 222 223 224 225
## 48076.09 55406.83 56309.96 79106.33 86611.52 88617.47 96219.13 87157.74
## 226 227 228 229 230 231 232 233
## 81148.02 80729.38 77374.80 76535.16 81278.61 82644.73 77072.64 72279.08
## 234 235 236 237 238 239 240 241
## 77940.40 64570.07 56595.76 48535.47 40135.65 44333.25 46488.90 39930.70
## 242 243 244 245 246 247 248 249
## 33601.69 43898.11 38137.20 42078.24 34331.61 33035.72 36695.97 39523.69
## 250 251 252 253 254 255 256 257
## 30340.71 36286.83 40093.41 45295.23 48017.23 47509.12 57824.04 75344.99
## 258 259 260 261 262 263 264 265
## 75159.79 68461.37 69945.22 66160.88 67608.98 61358.20 50618.27 46640.24
## 266 267 268 269 270 271 272 273
## 46849.93 42951.37 51767.46 48035.48 52187.84 50301.86 54374.74 54671.16
## 274 275 276 277 278 279 280 281
## 60696.76 58327.78 68013.87 61930.42 62140.73 60487.56 66238.23 59960.21
## 282 283 284 285 286 287 288 289
## 56519.40 46057.52 44452.09 61707.73 67245.33 67655.35 65069.43 64155.56
## 290 291 292 293 294 295 296 297
## 68161.96 72077.51 53048.23 43136.82 37139.72 47475.13 50721.37 49834.11
## 298 299 300 301 302 303 304 305
## 74063.34 80016.83 80685.40 85304.13 83500.25 78523.74 81995.29 56859.93
## 306 307 308 309 310 311 312 313
## 53115.96 52795.30 46577.00 43782.65 47390.91 39932.95 38653.30 33207.42
## 314 315 316 317 318 319 320 321
## 36997.13 36177.36 40014.06 37995.73 63817.27 61655.49 63281.30 71290.35
## 322 323 324 325 326 327 328 329
## 73680.94 99197.44 97572.89 73370.03 72166.23 70521.31 62462.02 59580.31
## 330 331 332 333 334 335 336 337
## 29487.57 33180.18 33620.31 35943.42 35283.35 41058.27 42135.35 37376.40
## 338 339 340 341 342 343 344 345
## 36589.75 36716.59 32029.12 38036.35 38700.67 38959.47 39836.48 41624.86
## 346 347 348 349 350 351 352 353
## 43449.36 43200.68 36135.62 26690.46 32042.49 30901.93 30491.42 28104.57
## 354 355 356 357 358 359 360 361
## 32789.91 36549.38 41019.15 39205.65 40467.68 42609.09 50014.56 50565.91
## 362 363 364 365 366 367 368 369
## 50767.85 53234.43 50714.37 50158.15 42793.17 39986.06 36157.74 33933.35
## 370 371 372 373 374 375 376 377
## 29985.68 37283.68 39573.80 47470.26 41433.54 40879.94 39415.22 38947.35
## 378 379 380 381 382 383 384 385
## 29802.36 34406.84 27448.88 35679.62 46027.03 49597.43 47868.40 49862.85
## 386 387 388 389 390 391 392 393
## 56091.64 65590.29 58792.15 53252.67 52981.00 60370.84 60894.95 69575.69
## 394 395 396 397 398 399 400 401
## 58666.19 60235.26 59794.75 59277.89 57762.69 56522.53 43262.66 51860.30
## 402 403 404 405 406 407 408 409
## 50859.33 49824.11 56234.35 48766.40 48076.59 46400.93 42072.12 40901.19
## 410 411 412 413 414 415 416 417
## 38962.34 33048.22 40932.05 43862.50 38527.34 33608.69 48501.23 52351.42
## 418 419 420 421 422 423 424 425
## 56286.22 48744.78 45062.92 43737.03 47328.55 35731.36 35460.18 29710.88
## 426 427 428 429 430 431 432 433
## 35308.60 43647.93 50549.79 47310.68 44375.23 41296.08 41184.11 37656.95
## 434 435 436 437 438 439 440 441
## 33789.89 31020.02 32598.34 34450.33 32452.13 37325.17 43539.46 40287.23
## 442 443 444 445 446 447 448 449
## 39992.81 43003.49 40886.57 44881.72 40121.90 31141.49 29977.31 41350.31
## 450 451 452 453 454 455 456 457
## 41031.40 46690.60 42320.55 42670.70 44293.51 48016.98 37876.89 42733.06
## 458 459 460 461 462 463 464 465
## 38149.82 45729.61 49254.64 51878.92 48603.82 50947.93 51149.75 52899.27
## 466 467 468 469 470 471 472 473
## 52396.16 55344.10 52668.58 57726.95 50977.30 48584.83 47168.97 43788.52
## 474 475 476 477 478 479 480 481
## 47549.99 55023.94 49442.97 51148.63 45930.06 44306.75 47143.72 36542.88
## 482 483 484 485 486 487 488 489
## 30095.28 31967.26 34667.77 36159.86 37127.10 30758.85 43306.90 49976.57
## 490 491 492 493 494 495 496 497
## 56816.57 51520.52 56360.45 64004.97 67821.80 54058.58 44623.04 42645.21
## 498 499 500 501 502 503 504 505
## 42968.87 43761.15 38240.04 40642.38 45951.43 51641.12 52351.80 52462.51
## 506 507 508 509 510 511 512 513
## 46155.50 47497.38 43751.28 46510.65 46175.86 39882.72 41015.41 40189.38
## 514 515 516 517 518 519 520 521
## 41296.58 43940.35 36770.32 32042.99 55966.55 64138.81 67796.93 61166.63
## 522 523 524 525 526 527 528 529
## 62508.01 76112.90 83099.48 58014.24 52877.03 49566.68 53951.86 53457.49
## 530 531 532 533 534 535 536 537
## 43627.19 48626.07 61304.46 55817.59 59220.04 63213.44 60261.87 55285.61
## 538 539 540 541 542 543 544 545
## 48738.66 47392.66 55340.97 55091.04 47640.59 49867.35 49693.77 50388.58
## 546 547 548 549 550 551 552 553
## 41024.40 32848.65 37196.58 45324.60 45121.28 46829.56 40830.96 49856.60
## 554 555 556 557 558 559 560 561
## 51013.54 40778.35 50332.72 58206.56 57570.61 61173.37 56934.66 68711.68
## 562 563 564 565 566 567 568 569
## 85423.72 75500.82 64048.34 68473.75 66595.51 67783.81 59342.00 43308.65
## 570 571 572 573 574 575 576 577
## 50327.10 56240.48 57424.90 59503.33 60151.28 57273.69 69537.45 58896.00
## 578 579 580 581 582 583 584 585
## 52610.22 60222.39 61728.47 54812.87 61088.15 56658.11 53698.55 67287.57
## 586 587 588 589 590 591 592 593
## 52695.83 60050.31 59141.43 52854.91 52159.10 52435.52 43138.45 45939.18
## 594 595 596 597 598 599 600 601
## 40558.16 44809.86 53585.33 46907.29 52774.43 55155.53 60832.47 56985.27
## 602 603 604 605 606 607 608 609
## 61805.70 53362.64 55245.25 56022.66 58334.03 58908.87 58449.00 52618.35
## 610 611 612 613 614 615 616 617
## 59689.78 57747.19 54840.36 51541.89 44495.08 56021.29 59914.09 50836.96
## 618 619 620 621 622 623 624 625
## 61253.35 65444.08 58924.87 81183.13 66285.35 58604.08 60609.78 55956.68
## 626 627 628 629 630 631 632 633
## 46278.71 57000.89 37530.36 37390.40 46971.90 57469.02 55510.93 84279.53
## 634 635 636 637 638 639 640 641
## 74452.23 76656.00 78295.55 73014.62 65723.88 62353.55 50241.87 48607.95
## 642 643 644 645 646 647 648 649
## 47498.00 45978.17 44360.61 47041.13 51664.86 66656.24 81100.28 78219.70
## 650 651 652 653 654 655 656 657
## 79124.70 85004.21 98467.27 93218.71 63448.13 60896.45 57846.66 58832.64
## 658 659 660 661 662 663 664 665
## 55268.74 45668.38 48055.97 52379.66 51570.76 63148.33 63025.87 63315.79
## 666 667 668 669 670 671 672 673
## 51755.46 53116.84 54136.68 49469.09 43440.62 46479.66 44075.19 47566.73
## 674 675 676 677 678 679 680 681
## 45316.10 38143.94 32794.66 32792.16 35544.91 40283.36 42546.86 40548.66
## 682 683 684 685 686 687 688 689
## 40572.28 41021.65 35356.33 41694.34 41191.11 41490.78 43488.98 54211.28
## 690 691 692 693 694 695 696 697
## 62680.09 70740.25 60022.57 56024.91 52903.02 58064.10 48343.77 42034.25
## 698 699 700 701 702 703 704 705
## 35921.68 32636.33 31114.25 36628.36 34889.59 35563.15 41504.15 70202.52
## 706 707 708 709 710 711 712 713
## 76369.45 93940.38 90254.64 92853.43 107898.93 106738.00 84069.22 84417.00
## 714 715 716 717 718 719 720 721
## 75743.63 72842.42 70816.23 53522.60 48914.74 52411.53 49912.96 39013.46
## 722 723 724 725 726 727 728 729
## 44588.93 40855.20 43103.33 40975.67 31670.47 35626.51 36252.59 29879.59
## 730 731 732 733 734 735 736 737
## 47971.50 50222.13 48253.42 53914.87 46311.33 46586.25 54578.31 41011.78
## 738 739 740 741 742 743 744 745
## 37419.02 45931.56 42701.82 44206.41 46978.77 46090.76 42505.12 49503.20
## 746 747 748 749 750 751 752 753
## 44517.69 65507.68 70834.97 66939.17 58863.88 78668.95 71734.10 70651.90
## 754 755 756 757 758 759 760 761
## 55869.70 104653.82 121746.75 126341.74 107843.44 110274.90 109521.36 107548.02
## 762 763 764 765 766 767 768 769
## 60163.90 45195.38 47107.11 45729.86 43703.79 152417.09 156859.74 182096.94
## 770 771 772 773 774 775 776 777
## 185668.09 179597.88 177592.68 184483.79 81567.78 72144.74 38475.73 41913.16
## 778 779 780 781 782 783 784 785
## 42322.67 46726.09 41117.63 41438.04 41280.46 45839.96 40570.40 40267.49
## 786 787 788 789 790
## 45388.08 48416.75 46668.98 44014.83 46757.08
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8128
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.941104 0.7633251 3.865037
## t2* 2557.341256 170.8395991 879.265829
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.027387 4.917448 12.86927
## 2 lag_depvar 1541.294040 2593.403819 4418.75306Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Dec 09 00:53:15 2024
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# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 6.190545 | 5.195333 | 5.410333 | 5.849167 | 6.413450 |
| Comida | 329.464273 | 366.009167 | 312.386750 | 317.896583 | 338.247017 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 88.688727 | 38.104750 | 47.072333 | 29.523000 | 43.320533 |
| Enceres | 26.169818 | 18.259750 | 24.219750 | 14.801167 | 24.056333 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.314383 |
| Gas/Bencina | 48.319273 | 42.636000 | 45.575000 | 13.583667 | 32.669133 |
| Diosi | 34.869000 | 55.804250 | 31.180667 | 52.687833 | 41.751467 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 17.992727 | 12.829167 | 25.156667 | 19.086917 | 18.535967 |
| Netflix | 1.517909 | 8.713833 | 7.151583 | 7.028750 | 6.506567 |
| Otros | 74.454545 | 5.481667 | 5.000000 | 0.000000 | 15.746333 |
| Total | 627.666818 | 563.738000 | 505.988083 | 474.453167 | 535.561183 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2535, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-12-09 00:04:58 sería de: 39.466 pesos// Percentil 95% más alto proyectado: 42.767,17
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 38485.83 | 38505.81 |
| Lo.80 | 38592.15 | 38706.47 |
| Point.Forecast | 39465.64 | 42152.82 |
| Hi.80 | 41324.69 | 46962.46 |
| Hi.95 | 42343.59 | 49508.53 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,1) with drift
##
## Coefficients:
## ma1 drift
## -0.8839 5.7699
## s.e. 0.1020 3.3908
##
## sigma^2 = 40236: log likelihood = -456.72
## AIC=919.44 AICc=919.81 BIC=926.09paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 xreg
## 0.3446 32.6260
## s.e. 0.1073 0.9385
##
## sigma^2 = 36564: log likelihood = -459.44
## AIC=924.88 AICc=925.25 BIC=931.58forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 985.2328 | 897.3811 | 834.2372 |
| Lo.80 | 1122.4442 | 1043.2030 | 938.5374 |
| Point.Forecast | 1381.6428 | 1318.6671 | 1171.6384 |
| Hi.80 | 1640.8413 | 1594.1312 | 1463.2301 |
| Hi.95 | 1778.0527 | 1739.9531 | 1649.7874 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))